Data Synthesis

Data synthesis focuses on generating artificial datasets that mimic the statistical properties and structure of real-world data, primarily to address data scarcity, privacy concerns, and the need for diverse training data in machine learning. Current research emphasizes the synthesis of complex data types, including relational databases and time series, often employing generative models like diffusion models and large language models (LLMs) to achieve high fidelity and utility. These techniques are proving valuable in various applications, from improving the performance of large language models and vision systems to enhancing medical image analysis and enabling privacy-preserving data sharing. The field is also actively developing robust evaluation metrics and methods to ensure the quality and reliability of synthetic data.